The present invention relates to a system and method for determining reliability and forecasting, with an ascertained statistical confidence, a remaining time before failure for electric motor systems including an insulation condition monitor.
Acquisition of accurate information concerning the running condition, status and performance of motor systems, such as, for example, electric motors used in “critical” industrial manufacturing processes, power generation systems and the like, is often crucial in maintaining successful commercial operation of such systems. Consequently, considerable efforts are often expended to develop and improve upon existing methods and apparatuses used for monitoring and assessing the operation and performance of electric motors and coil devices in such systems. Robust methods of inspection are often desired for such critical process motors, since if a motor must be taken off-line, its inoperability may adversely impact production and manufacturing processes or other revenue generating capacity.
Robust processes for the inspection and predictive maintenance of motor systems usually involve monitoring a variety of operational parameters such as motor current, voltage, vibration, flux leakage, etc. to detect impending failures. Conventionally, one or more parameters are monitored over time and used to trigger a maintenance outage/recommendation when the value of a monitored parameter exceeds a predetermined threshold. The contemporary technological trend is to automate the inspection process by affixing a variety of sensors and transducers to critical process motors to continuously collect information through either off-line monitoring or on-line monitoring techniques. Parameter data for an operating motor may then be tracked continuously and an alarm may be immediately triggered if a predetermined threshold value for a particular parameter is exceeded. For example, vibration amplitude or spectral data that exceeds or drifts from a predetermined range or value can be used to activate an alarm to notify the equipment operator that a particular type of failure mode is imminent. Unfortunately, these conventional inspection and predictive maintenance processes typically target only imminent failures and do not provide a quantitative determination of remaining motor life or motor reliability.
In general, service and repair information acquired as a result of previous inspections and routine maintenance of motor equipment is not compiled for the purpose of performing predictive/prognostic maintenance or conducting a comprehensive analysis of motor health. Conventionally, a motor system expert/specialist simply assesses available historical information and then formulates a maintenance recommendation based on obvious trends and personal experience. A decision to repair or perform maintenance on a particular motor system was based on an estimate of the reliability and usability of the equipment developed primarily from the expert's subjective judgment. In other instances, preventive maintenance is based solely on the number of hours of motor operation or the time since the last maintenance outage, rather than on any condition-based test results. Moreover, even if it was desirable for a motor operator/technician or monitoring specialist to collect test data or parametric operating data from a particular motor system for performing a more detailed analysis, access to conventional digital land line communications for uploading such data is often not available at the motor system site.
The use of motor operational parameter data as a failure predictive tool and to assess motor health has been explored to some extent in the past by various investigators. Different motor system parameters may be used for this purpose and may include motor system “unbalances” such as negative sequence currents, and voltage mismatch. In one example, the Fast Fourier Transform (FFT) signature of motor current was shown capable of detecting motor bearing failures. In another example, an algorithm for performing cluster analysis on the motor supply current FFT was investigated in the hopes of predicting motor life uncertainty. However, most known conventional methods provide only a general warning of imminent motor failure based on the detection of an alarm condition from a single monitored parameter. Typically, such methods do not provide an assessment of motor reliability, nor do they provide an estimate of the operating time remaining until a repair will be needed.
Given the problems discussed above, it is desirable to design a method and system capable of gathering and analyzing on-line motor parameters. It is also desirable to provide cost effective methods for transmitting motor parameters between a motor system and a local area network. Additionally, it is desirable to develop a method and system capable of gathering and analyzing on-line motor parameters that enable a prediction of motor reliability and estimated operating time until a repair will be needed based on an insulation condition of the motor system.